Abstract

The rapid integration of data-driven technologies in education has created new opportunities for optimizing curriculum design and enhancing student assessment; however, the opaque nature of many advanced machine learning models raises concerns about fairness, accountability, and trust. This review paper examines the application of Explainable Machine Learning (XML) models to educational data analytics, emphasizing their role in providing transparent and interpretable decision support for curriculum development and student performance evaluation. The study synthesizes existing literature on interpretable algorithms, model-agnostic explanation techniques such as SHAP and LIME, and inherently transparent models including decision trees and rule-based classifiers within educational contexts. It explores how explainability mechanisms can uncover hidden learning patterns, identify at-risk students, evaluate instructional effectiveness, and support evidence-based curriculum adjustments while ensuring ethical compliance and stakeholder trust. Furthermore, the review analyzes key challenges such as bias mitigation, data privacy, scalability, and the trade-off between model accuracy and interpretability. By integrating insights from artificial intelligence, learning analytics, and educational policy frameworks, this paper provides a comprehensive foundation for deploying explainable machine learning systems that promote fairness, transparency, and informed decision-making in modern educational environments.